SoroushVahidi/scidocs huggingface dataset:
Smart vehicle accident detection and alarming system using a smartphone
(2015)

Vehicle accident is the paramount thread for the people's life which causes a serious wound or even dead. The automotive companies have made lots of progress in alleviating this thread, but still the probability of detrimental effect due to an accident is not reduced. Infringement of spFieed is one of the elementary reasons for a vehicle accident. Therewithal, external pressure and change of tilt angle with road surface blameworthy for this mishap. As soon as the emergency service could divulge about an accident, the more the effect would be mitigated. For this purpose, we developed an Android based application that detects an accidental situation and sends emergency alert message to the nearest police station and health care center. This application is integrated with an external pressure sensor to extract the outward force of the vehicle body. It measures speed and change of tilt angle with GPS and accelerometer sensors respectively on Android phone. By checking conditions, this application also capable of reducing the rate of false alarm.
Robust Automatic Measurement of 3D Scanned Models for the Human Body Fat Estimation
(2015)

In this paper, we present an automatic tool for estimating geometrical parameters from 3-D human scans independent on pose and robustly against the topological noise. It is based on an automatic segmentation of body parts exploiting curve skeleton processing and ad hoc heuristics able to remove problems due to different acquisition poses and body types. The software is able to locate body trunk and limbs, detect their directions, and compute parameters like volumes, areas, girths, and lengths. Experimental results demonstrate that measurements provided by our system on 3-D body scans of normal and overweight subjects acquired in different poses are highly correlated with the body fat estimates obtained on the same subjects with dual-energy X-rays absorptiometry (DXA) scanning. In particular, maximal lengths and girths, not requiring precise localization of anatomical landmarks, demonstrate a good correlation (up to 96%) with the body fat and trunk fat. Regression models based on our automatic measurements can be used to predict body fat values reasonably well.
Asymmetrical impact of trustworthiness attributes on trust, perceived value and purchase intention: a conceptual framework for cross-cultural study on consumer perception of online auction
(2003)

Lack of trust has been frequently cited to be one of the key factors that discourage customers from participating in e-commerce, while cultural differences affect the formation of trust. As such, this cross-cultural study aims at establishing a conceptual framework to investigate the primary antecedents of online trust and the mechanism that converts consumer trust into perceived value and purchase intention in online auction. Two elements of our model bear mention here. First, the authors divide the consumer trust into two facets, seller and intermediary trust, and intend to assess their reciprocal relationships. Second, the authors intend to examine the asymmetrical impact of the negative and positive trustworthiness attributes on trust, and the interrelationship among trust, perceived value and purchase intention across cultural groups.
Combining Classifiers for Chinese Word Segmentation
(2002)

In this paper we report results of a supervised machine-learning approach to Chinese word segmentation. First, a maximum entropy tagger is trained on manually annotated data to automatically labels the characters with tags that indicate the position of character within a word. An error-driven transformation-based tagger is then trained to clean up the tagging inconsistencies of the first tagger. The tagged output is then converted into segmented text. The preliminary results show that this approach is competitive compared with other supervised machine-learning segmenters reported in previous studies.
Deep Transfer in Reinforcement Learning by Language Grounding
(2017)

In this paper, we explore the utilization of natural language to drive transfer for reinforcement learning (RL). Despite the wide-spread application of deep RL techniques, learning generalized policy representations that work across domains remains a challenging problem. We demonstrate that textual descriptions of environments provide a compact intermediate channel to facilitate effective policy transfer. We employ a model-based RL approach consisting of a differentiable planning module, a model-free component and a factorized representation to effectively utilize entity descriptions. Our model outperforms prior work on both transfer and multi-task scenarios in a variety of different environments.
Demystifying automotive safety and security for semiconductor developer
(2017)

Advances in both semiconductor and automotive industry are today enabling the next generation of vehicles with significant electronics content than ever before. Consumers can now avail vehicle offerings in the form of Electric and Hybrid Electric Vehicles (EV/HEV) that have improved fuel efficiency, provide enhanced driver-passenger comfort and experience through Advance Driver Assistance Systems (ADAS) and car infotainment systems, and more. Increasing electronics, software content, and connectivity drive two consumer concerns — “functional safety” and “security” — to the forefront. In this tutorial, we dissect these concerns from an end application perspective and translate the system level requirements and standards into semiconductor development requirements. We indicate both current and emerging practices, and touch upon areas requiring new or optimal design and electronic design automation (EDA) solutions. While functional safety is the primary focus for deep-dive in this tutorial, we also examine key facets of automotive security which is now emerging as a critical area for further understanding and standardization.
Detecting influenza epidemics using search engine query data
(2009)

Seasonal influenza epidemics are a major public health concern, causing tens of millions of respiratory illnesses and 250,000 to 500,000 deaths worldwide each year. In addition to seasonal influenza, a new strain of influenza virus against which no previous immunity exists and that demonstrates human-to-human transmission could result in a pandemic with millions of fatalities. Early detection of disease activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza. One way to improve early detection is to monitor health-seeking behaviour in the form of queries to online search engines, which are submitted by millions of users around the world each day. Here we present a method of analysing large numbers of Google search queries to track influenza-like illness in a population. Because the relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms, we can accurately estimate the current level of weekly influenza activity in each region of the United States, with a reporting lag of about one day. This approach may make it possible to use search queries to detect influenza epidemics in areas with a large population of web search users.
Freebase: a collaboratively created graph database for structuring human knowledge
(2008)

Freebase is a practical, scalable tuple database used to structure general human knowledge. The data in Freebase is collaboratively created, structured, and maintained. Freebase currently contains more than 125,000,000 tuples, more than 4000 types, and more than 7000 properties. Public read/write access to Freebase is allowed through an HTTP-based graph-query API using the Metaweb Query Language (MQL) as a data query and manipulation language. MQL provides an easy-to-use object-oriented interface to the tuple data in Freebase and is designed to facilitate the creation of collaborative, Web-based data-oriented applications.
Capturing Evolving Visit Behavior in Clickstream Data
(2000)

Many online sites, both retailers and content providers, routinely monitor visitor traffic as a useful measure of their overall success. However, simple summaries such as the total number of visits per month provide little insight about individual-level site-visit patterns, especially in a changing environment such as the Internet. This article develops an individual-level model for evolving visiting behavior based on Internet clickstream data. We capture cross-sectional variation in site-visit behavior as well as changes over time as visitors gain experience with the site. In addition, we examine the relationship between visiting frequency and purchasing propensity at an e-commerce site. We find evidence supporting the notion that people who visit a retail site more frequently have a greater propensity to buy. We also show that changes (i.e., evolution) in an individual's visit frequency over time provides further information regarding which customer segments are likely to have higher purchasing conversion rates. Disciplines Business | Business Analytics | E-Commerce | Management Sciences and Quantitative Methods | Marketing | Technology and Innovation This technical report is available at ScholarlyCommons: https://repository.upenn.edu/marketing_papers/275 Capturing Evolving Visit Behavior in Clickstream Data Wendy W. Moe and Peter S. Fader The Wharton School University of Pennsylvania Marketing Department 1400 Steinberg Hall-Dietrich Hall Philadelphia, PA 19104-6371 telephone: (215) 898-7603 fax: (215) 898-2534 e-mail: wendy@marketing.wharton.upenn.edu
Automatic Generation of Personas Using YouTube Social Media Data
(2017)

We develop and implement an approach for automating generating personas in real time using actual YouTube social media data from a global media corporation. From the organization's YouTube channel, we gather demographic data, customer interactions, and topical interests, leveraging more than 188,000 subscriber profiles and more than 30 million user interactions. We demonstrate that online user data can be used to develop personas in real time using social media analytics. To get diverse aspects of personas, we collect user data from other social media channels as well and match them with the corresponding user data to generate richer personas. Our results provide corporate insights into competitive marketing, topical interests, and preferred product features for the users of the online news medium. Research implications are that reasonably rich personas can be generated in real time, instead of being the result of a laborious and time-consuming manual development process.
Dual Encoding for Abstractive Text Summarization.
(2018)

Recurrent neural network-based sequence-to-sequence attentional models have proven effective in abstractive text summarization. In this paper, we model abstractive text summarization using a dual encoding model. Different from the previous works only using a single encoder, the proposed method employs a dual encoder including the primary and the secondary encoders. Specifically, the primary encoder conducts coarse encoding in a regular way, while the secondary encoder models the importance of words and generates more fine encoding based on the input raw text and the previously generated output text summarization. The two level encodings are combined and fed into the decoder to generate more diverse summary that can decrease repetition phenomenon for long sequence generation. The experimental results on two challenging datasets (i.e., CNN/DailyMail and DUC 2004) demonstrate that our dual encoding model performs against existing methods.
World of Warcraft and the impact of game culture and play in an undergraduate game design course
(2011)

During the past two decades, digital games have become an increasingly popular source of study for academics, educational researchers and instructional designers. Much has been written about the potential of games for teaching and learning, both in the design of educational/serious games and the implementation of off-the-shelf games for learning. Yet relatively little research has been conducted about how game culture and the enmeshed practice of play may impact classroom dynamics. The purpose of this study is to present a case study about how the use of World of Warcraft (WoW) as a teaching tool and medium of play impacted class dynamics in an undergraduate university-level course for game design. Specifically, this study will address how WoW’s game culture and the practice of play impacted (a) student-to-student dynamics and (b) class dynamics. The goal of this study is to explore some of the dynamics of play as a component of learning. 2010 Elsevier Ltd. All rights reserved.
(MP)2T: Multiple People Multiple Parts Tracker
(2012)

We present a method for multi-target tracking that exploits the persistence in detection of object parts. While the implicit representation and detection of body parts have recently been leveraged for improved human detection, ours is the first method that attempts to temporally constrain the location of human body parts with the express purpose of improving pedestrian tracking. We pose the problem of simultaneous tracking of multiple targets and their parts in a network flow optimization framework and show that parts of this network need to be optimized separately and iteratively, due to inter-dependencies of node and edge costs. Given potential detections of humans and their parts separately, an initial set of pedestrian tracklets is first obtained, followed by explicit tracking of human parts as constrained by initial human tracking. A merging step is then performed whereby we attempt to include part-only detections for which the entire human is not observable. This step employs a selective appearance model, which allows us to skip occluded parts in description of positive training samples. The result is high confidence, robust trajectories of pedestrians as well as their parts, which essentially constrain each other’s locations and associations, thus improving human tracking and parts detection. We test our algorithm on multiple real datasets and show that the proposed algorithm is an improvement over the state-of-the-art.
Normal vulvovaginal, perineal, and pelvic anatomy with reconstructive considerations.
(2011)

A thorough insight into the female genital anatomy is crucial for understanding and performing pelvic reconstructive procedures. The intimate relationship between the genitalia and the muscles, ligaments, and fascia that provide support is complex, but critical to restore during surgery for correction of prolapse or aesthetic reasons. The external female genitalia include the mons pubis, labia majora and minora, clitoris, vestibule with glands, perineal body, and the muscles and fascia surrounding these structures. Through the perineal membrane and the perineal body, these superficial vulvar structures are structurally related to the deep pelvic muscle levator ani with its fascia. The levator ani forms the pelvic floor with the coccygeus muscle and provides vital support to all the pelvic organs and stability to the perineum. The internal female genital organs include the vagina, cervix, uterus, tubes, and ovaries with their visceral fascia. The visceral fascia also called the endopelvic fascia, surrounds the pelvic organs and connects them to the pelvic walls. It is continuous with the paraurethral and paravaginal fascia, which is attached to the perineal membrane. Thus, the internal and external genitalia are closely related to the muscles and fascia, and work as one functioning unit.
Automatic Color Calibration for Large Camera Arrays
(2005)

We present a color calibration pipeline for large camera arrays. We assume static lighting conditions for each camera, such as studio lighting or a stationary array outdoors. We also assume we can place a planar calibration target so it is visible from every camera. Our goal is uniform camera color responses, not absolute color accuracy, so we match the cameras to each other instead of to a color standard. We first iteratively adjust the color channel gains and offsets for each camera to make their responses as similar as possible. This step white balances the cameras, and for studio applications, ensures that the range of intensities in the scene are mapped to the usable output range of the cameras. Residual errors are then calibrated in post-processing. We present results calibrating an array of 100 CMOS image sensors in different physical configurations, including closely or widely spaced cameras with overlapping fields of views, and tightly packed cameras with non-overlapping fields of view. The process is entirely automatic, and the camera configuration runs in less than five minutes on the 100 camera array. CR Categories: I.4.1 [Image Processing and Computer Vision]: Digitization and Image Capture.Camera Calibration;
Learning values across many orders of magnitude
(2016)

Most learning algorithms are not invariant to the scale of the function that is being approximated. We propose to adaptively normalize the targets used in learning. This is useful in value-based reinforcement learning, where the magnitude of appropriate value approximations can change over time when we update the policy of behavior. Our main motivation is prior work on learning to play Atari games, where the rewards were all clipped to a predetermined range. This clipping facilitates learning across many different games with a single learning algorithm, but a clipped reward function can result in qualitatively different behavior. Using the adaptive normalization we can remove this domain-specific heuristic without diminishing overall performance.
Propagation Models for Trust and Distrust in Social Networks
(2005)

Semantic Web endeavors have mainly focused on issues pertaining to knowledge representation and ontology design. However, besides understanding information metadata stated by subjects, knowing about their credibility becomes equally crucial. Hence, trust and trust metrics, conceived as computational means to evaluate trust relationships between individuals, come into play. Our major contribution to Semantic Web trust management through this work is twofold. First, we introduce a classification scheme for trust metrics along various axes and discuss advantages and drawbacks of existing approaches for Semantic Web scenarios. Hereby, we devise an advocacy for local group trust metrics, guiding us to the second part which presents Appleseed, our novel proposal for local group trust computation. Compelling in its simplicity, Appleseed borrows many ideas from spreading activation models in psychology and relates their concepts to trust evaluation in an intuitive fashion. Moreover, we provide extensions for the Appleseed nucleus that make our trust metric handle distrust statements.
The Human Element in Autonomous Vehicles
(2017)

Autonomous vehicle research has been prevalent for well over a decade but only recently has there been a small amount of research conducted on the human interaction that occurs in autonomous vehicles. Although functional software and sensor technology is essential for safe operation, which has been the main focus of autonomous vehicle research, handling all elements of human interaction is also a very salient aspect of their success. This paper will provide an overview of the importance of human vehicle interaction in autonomous vehicles, while considering relevant related factors that are likely to impact adoption. Particular attention will be given to prior research conducted on germane areas relating to control in the automobile, in addition to the different elements that are expected to affect the likelihood of success for these vehicles initially developed for human operation. This paper will also include a discussion of the limited research conducted to consider interactions with humans and the current state of published functioning software and sensor technology that exists.
Data and Code for "Automatic Identification of Narrative Diegesis and Point of View"
(2016)

The style of narrative news affects how it is interpreted and received by readers. Two key stylistic characteristics of narrative text are point of view and diegesis: respectively, whether the narrative recounts events personally or impersonally, and whether the narrator is involved in the events of the story. Although central to the interpretation and reception of news, and of narratives more generally, there has been no prior work on automatically identifying these two characteristics in text. We develop automatic classifiers for point of view and diegesis, and compare the performance of different feature sets for both. We built a goldstandard corpus where we double-annotated to substantial agreement (κ > 0.59) 270 English novels for point of view and diegesis. As might be expected, personal pronouns comprise the best features for point of view classification, achieving an average F1 of 0.928. For diegesis, the best features were personal pronouns and the occurrences of first person pronouns in the argument of verbs, achieving an average F1 of 0.898. We apply the classifier to nearly 40,000 news texts across five different corpora comprising multiple genres (including newswire, opinion, blog posts, and scientific press releases), and show that the point of view and diegesis correlates largely as expected with the nominal genre of the texts. We release the training data and the classifier for use by the community.
A fast, invariant representation for human action in the visual system.
(2018)

Humans can effortlessly recognize others' actions in the presence of complex transformations, such as changes in viewpoint. Several studies have located the regions in the brain involved in invariant action recognition; however, the underlying neural computations remain poorly understood. We use magnetoencephalography decoding and a data set of well-controlled, naturalistic videos of five actions (run, walk, jump, eat, drink) performed by different actors at different viewpoints to study the computational steps used to recognize actions across complex transformations. In particular, we ask when the brain discriminates between different actions, and when it does so in a manner that is invariant to changes in 3D viewpoint. We measure the latency difference between invariant and noninvariant action decoding when subjects view full videos as well as form-depleted and motion-depleted stimuli. We were unable to detect a difference in decoding latency or temporal profile between invariant and noninvariant action recognition in full videos. However, when either form or motion information is removed from the stimulus set, we observe a decrease and delay in invariant action decoding. Our results suggest that the brain recognizes actions and builds invariance to complex transformations at the same time and that both form and motion information are crucial for fast, invariant action recognition. NEW & NOTEWORTHY The human brain can quickly recognize actions despite transformations that change their visual appearance. We use neural timing data to uncover the computations underlying this ability. We find that within 200 ms action can be read out of magnetoencephalography data and that this representation is invariant to changes in viewpoint. We find form and motion are needed for this fast action decoding, suggesting that the brain quickly integrates complex spatiotemporal features to form invariant action representations.